MLOps : Building an Open Source Machine Learning Platform
Emu (Nurin) | Wed 17 Apr 3:45 p.m.–4:30 p.m.
Presented by
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Faisal Masood is a cloud transformation architect at AWS. Faisal focus is to assist customers in refining and executing strategic business goals. Faisal main interests are evolutionary architectures, software development, ML lifecycle, CD and IaC. Faisal has over two decades of experience in software architecture and development.
Faisal has completed his engineering studies at NED University and has completed continuing education courses at MIT Sloan and the University of New Mexico. Faisal published two books[1][2] on MLOps and Kubernetes, developed a free Kubernetes course at 10perals university and routinely writes blogs at Red Hat Developer Blog.
[1] Machine Learning on Kubernetes - https://www.amazon.com/Machine-Learning-Kubernetes-practical-handbook-ebook/dp/B09WF2B1BX?ref_=ast_author_mpb
[2] The Kubernetes Workshop - https://www.amazon.com/Kubernetes-Workshop-Interactive-Approach-Learning-ebook/dp/B082VFMMTY?ref_=ast_author_dp
Abstract
With the advancement of ML algorithms and data availability, more and more organisations are using ML to help uplift their businesses. But ML is not just building models. It includes steps such as data preparation, model training, model testing, model deployment, and monitoring and how to automate these steps to keep your models useful for the business
In this session you will see how OSS powers the ML development and deployment process with the use of automation, and monitoring tools to ensure that models remain accurate, stable, and scalable over time. You will build an end to end model workflow where data scientists, machine learning engineers, software developers, and operations teams collaborate and create a continuous integration and delivery pipeline for machine learning models.
With the advancement of ML algorithms and data availability, more and more organisations are using ML to help uplift their businesses. But ML is not just building models. It includes steps such as data preparation, model training, model testing, model deployment, and monitoring and how to automate these steps to keep your models useful for the business In this session you will see how OSS powers the ML development and deployment process with the use of automation, and monitoring tools to ensure that models remain accurate, stable, and scalable over time. You will build an end to end model workflow where data scientists, machine learning engineers, software developers, and operations teams collaborate and create a continuous integration and delivery pipeline for machine learning models.